Workload based order acceptance in job shop environments

TLDR: This work investigates the importance of a good workload based order acceptance method in over-demanded job shop environments, and presents sophisticated methods that consider technological restrictions, such as precedence relations, and release and due dates of orders.

Abstract: In practice, order acceptance and production planning are often functionally separated As a result, order acceptance decisions are made without considering the actual workload in the production system, or by only regarding the aggregate workload We investigate the importance of a good workload based order acceptance method in over-demanded job shop environments, and study approaches that integrate order acceptance and resource capacity loading We present sophisticated methods that consider technological restrictions, such as precedence relations, and release and due dates of orders We use a simulation model of a generic job shop to compare these methods with straightforward methods, which consider capacity restrictions at an aggregate level and ignore precedence relations We compare the performance of the approaches based on criteria such as capacity utilisation The simulation results show that the sophisticated approaches significantly outperform the straightforward approaches in case of tight due dates (little slack) In that case, improvements of up to 30% in utilisation rate can be achieved In case of much slack, a sophisticated order acceptance method is less important